Naive Bayes

Creates a NaiveBayes model. Supports both Multinomial NB which can handle finitely supported discrete data. For example, by converting documents into TF-IDF vectors, it can be used for document classification. By making every vector a binary (0/1) data, it can also be used as Bernoulli NB.The input feature values must be nonnegative

Type

ml-estimator

Class

fire.nodes.ml.NodeNaiveBayes

Fields

Name	Title	Description
featuresCol	Features Column	Features column of type vectorUDT for model fitting
labelCol	Label Column	The label column for model fitting
predictionCol	Prediction Column	The prediction column created during model scoring
splitRatio	Split Ratio	Split Ratio
path	Path	Save Confusion Matrix to Path
modelType	modelType	The model type. Supported options: multinomial and bernoulli. (default = multinomial)
smoothing	Smoothing	The smoothing parameter.
weightCol	Weight Column	Weight Column

Details

Naive Bayes classifiers are a family of simple probabilistic, multiclass classifiers based on applying Bayes’ theorem with strong (naive) independence assumptions between every pair of features.

Naive Bayes can be trained very efficiently. With a single pass over the training data, it computes the conditional probability distribution of each feature given each label. For prediction, it applies Bayes’ theorem to compute the conditional probability distribution of each label given an observation.

More details are available at Apache Spark ML docs page:

http://spark.apache.org/docs/latest/ml-classification-regression.html#naive-bayes

Examples

Below example is available at : https://spark.apache.org/docs/latest/ml-classification-regression.html#naive-bayes

import org.apache.spark.ml.classification.NaiveBayes

import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator

// Load the data stored in LIBSVM format as a DataFrame.

val data = spark.read.format(“libsvm”).load(“data/mllib/sample_libsvm_data.txt”)

// Split the data into training and test sets (30% held out for testing)

val Array(trainingData, testData) = data.randomSplit(Array(0.7, 0.3), seed = 1234L)

// Train a NaiveBayes model.

val model = new NaiveBayes()

.fit(trainingData)

// Select example rows to display.

val predictions = model.transform(testData)

predictions.show()

// Select (prediction, true label) and compute test error

val evaluator = new MulticlassClassificationEvaluator()

.setLabelCol(“label”)

.setPredictionCol(“prediction”)

.setMetricName(“accuracy”)

val accuracy = evaluator.evaluate(predictions)

println(s”Test set accuracy = $accuracy”)